How to create a Q-Learning agent when we have a matrix as an action space?
I was thinking this strategy may work.
So, Q-learning takes vector input as state representation let's say your vector has n dimensions i.e. [$n_0$, $n_1$, $n_2$,..., $n_{n-1}$]
Now, from my interpretation you want to populate a matrix with 0 and 1's given the state vector but action-space has a high complexity e.g. an 8*8 matrix has 64 cells i.e. $2^{64}$ possible actions if you want the action to be a matrix.
I suggest this:
Fill each cell, one at a time. i.e. Your agent has only two possible action 0 and 1.
To indicate to your agent that you are at a specific cell, concatenate the row and column number to the state vector before passing it as input to the Q-learning agent.
Example:
If you original state-vector is [55, 22, 100, 4] and you have to fill cell at position (10, 30) of the matrix, the state-vector should be modified as follows:
[55, 22, 100, 4, 10, 30].
I'm not sure of the sample-efficiency of this approach though.